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Record W3138537787 · doi:10.1299/jsmemecj.2020.j10206

Influence of sound pressure and grazing flow on the acoustic resistance of resonator orifice

2020· article· en· W3138537787 on OpenAlexaff
Takayasu TORIGOE, Shotaro YAMAZAKI, Keisuke MIYASHITA, Ko Nakayama, Tomohito Nakamori, Masaharu Nishimura, Takashi Matsuno, Tonau NAKAI, Tomonobu GOTO

Bibliographic record

VenueThe Proceedings of Mechanical Engineering Congress Japan · 2020
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsBody orificeMach numberAcoustic impedanceDuct (anatomy)AcousticsParticle velocitySound pressureResonatorPhysicsMicrophoneFlow velocityMaterials scienceMechanicsFlow (mathematics)OpticsEngineeringAnatomy

Abstract

fetched live from OpenAlex

Acoustic impedance of a rectangular orifice of a resonator on a side wall of a duct was measured. The measurement was based on two-microphone-method, in which the acoustic impedance was calculated from the transfer function of sound pressures at the end of the resonator and at the outlet of the orifice. The acoustic impedance, especially the resistance θp at resonance frequency fp, depends on the particle velocity mach number undulating through the orifice M0 and the flow speed mach number in the duct grazing over the orifice M. When the grazing flow speed mach number M is small, the resistance θp mainly increases with the particle velocity mach number M0. On the other hand, when M is large, θp increases with M and is almost independent of M0. In the latter state, the sound pressure at the resonator end decreases with M while the sound pressure at the orifice slightly increases with M, which results in the increase in θp. Those two states are roughly classified by use of the ratio M/M0; M/M0 is lower or higher than around 3.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.188
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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